The present invention relates to the field of content tagging and, more particularly, to tagging graphic objects by determining a set of similar pre-tagged graphic objects and extracting prominent tags from that set.
Digital content tagging is frequently implemented in software to allow for fast and efficient searching of the content. Tags are associated with digital content and they are commonly descriptors of the content's semantic content. Digital content can include images, video, and other digital content that are not easily searched. To enable searching, tags can be searched rather than the content itself. The biggest problem faced in tagging digital content is finding relevant tags for all of the content. Many systems rely on users to provide relevant semantic tags for content. This approach can be error prone and extremely time consuming.
A number of solutions exist for automatic content tagging of images or graphic objects, such as digital pictures. One technique is to perform object recognition to recognize discrete items visually rendered within an image and to then to add a tag for the recognized object to the image. Object recognition from an image can be processor and memory intensive. Additionally, many recognizable items in an image are relatively unimportant from an image tagging perspective.